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AI モデル 2119
- Sulphur-2-base↗
SulphurAI
An uncensored video generation model based on LTX 2.3 supporting both t2v and i2v natively, as well as all of the other ltx 2.3 formats. Support the next version of the project, even just a few dollars would go a long way: **Kofi** **Get Started:** To get started with the model, I recommend downloading either of the dev versions, (fp8mixed or bf16) and downloading the distill lora provided. By the way, I'm aware the workflows contain sulphur_final right now, just use the lora or use the full models, don't use both at the same time.
- Ternary-Bonsai-2-27B-Abliterated-v2-PQ2_0-MTP-GGUF↗
BoldingBuilds
**v2 is built to be run with thinking on.** Abliterated Bonsai 2 builds, my own v1 included, tend to write the whole answer inside their reasoning and run out of budget before handing it over. v2 changes one row of the output layer so the model closes its reasoning once the answer is ready. With `reasoning_effort` set to medium it answered every prompt on the main test; at default settings it halves v1's blank answers. **Where v2 stands out: hard requests, given room to think.** StrongReject (150 detailed harmful requests), thinking on, default settings, 16,384-token budget, same judge for every row: Much of that gap is answers the other builds never deliver: they write the answer inside their reasoning and run out of room before closing it.
- t5-base↗
google-t5
1. Model Details 2. Uses 3. Bias, Risks, and Limitations 4. Training Details 5. Evaluation 6. Environmental Impact 7. Citation 8. Model Card Authors 9. How To Get Started With the Model The developers of the Text-To-Text Transfer Transformer (T5) write: > With T5, we propose reframing all NLP tasks into a unified text-to-text-format where the input and output are always text strings, in contrast to BERT-style models that can only output either a class label or a span of the input. Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task.
- LFM2.5-24B-A2B-GGUF↗
LiquidAI
LFM2 is a new generation of hybrid models developed by Liquid AI, specifically designed for edge AI and on-device deployment. It sets a new standard in terms of quality, speed, and memory efficiency. Find more details in the original model card: https://huggingface.co/LiquidAI/LFM2.5-24B-A2B The sampling parameters are the ones recommended for the base model (its `generation_config.json`); each GGUF also carries them as `general.sampling.*` metadata, so llama.cpp applies them by default.
- MiniMax-H3-TextEncoder-Qwen3VL-32B-abliterated-GGUF↗
pottokao
> 🆕 **Newer, better-quality build:** pottokao/MiniMax-H3-TextEncoder-16GB-GGUF — official encoder weights, quantized per tensor by how much each one affects the H3 DiT, K-quants only. At the same 12.65 GB `_vis` size its conditioning error is about half of this Q3_K_S (7.9% vs 14.4%), with 13.7 / 14.8 GB options at 6.4% / 4.9%. > ⚠️ The `_vis` file here stores the visual tower's 5-D `patch_embed` weight as-is; recent `gguf` versions refuse tensors with more than 4 dimensions, so it may fail to load on an up-to-date ComfyUI-GGUF. The new repo stores it the ComfyUI-GGUF way. > 🆕 有新版：pottokao/MiniMax-H3-TextEncoder-16GB-GGUF（官方權重、依 H3 DiT 敏感度逐張量量化、只用 K 系列）。同樣 12.65 GB 誤差約為這顆的一半。這裡的 `_vis` 檔在新版 gguf 套件上可能載不進去。 > 📌 Heads-up: I've been focused on still-image work lately, so I don't have the bandwidth to help with video setups like MiniMax-H3 for now. The files stay up as they are — thanks for understanding! > 最近重心放在平面攝影，影片類（像 MiniMax-H3）暫時比較沒辦法協助排錯。檔案會照舊留著，謝謝體諒！ GGUF (llama.cpp) builds of the **MiniMax-H3 text encoder** — abliterated Qwen3-VL-32B, 50 layers, **Q3_K_S** (≈3.53 BPW).
- controlnet-union-sdxl-1.0↗
xinsir
- Use bucket training like novelai, can generate high resolutions images of any aspect ratio - Use large amount of high quality data(over 10000000 images), the dataset covers a diversity of situation - Use re-captioned prompt like DALLE.3, use CogVLM to generate detailed description, good prompt following ability - Use many useful tricks during training. Including but not limited to date augmentation, mutiple loss, multi resolution - Use almost the same parameter compared with original ControlNet. No obvious increase in network parameter or computation. - Support 10+ control conditions, no obvious performance drop on any single condition compared with training independently - Support multi condition generation, condition fusion is learned during training. No need to set hyperparameter or design prompts. - Compatible with other opensource SDXL models, such as BluePencilXL, CounterfeitXL. Compatible with other Lora models. ***We design a new architecture that can support 10+ control types in condition text-to-image generation and can generate high resolution images visually comparable with midjourney***. The network is based on the original ControlNet architecture, we propose two new modules to: 1 Extend the original ControlNet to support different image conditions using the same network parameter. 2 Support multiple conditions input without increasing computation offload, which is especially important for designers who want to edit image in detail, different conditions use the same condition encoder, without adding extra computations or parameters. We do thoroughly experiments on SDXL and achieve superior performance both in control ability and aesthetic score. We release the method and the model to the open source community to make everyone can enjoy it. Inference scripts and more details can found: https://github.com/xinsir6/ControlNetPlus/tree/main
- Yi-1.5-6B↗
01-ai
Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples. Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension. Model | Context Length | Pre-trained Tokens | :------------: | :------------: | :------------: | | Yi-1.5 | 4K, 16K, 32K | 3.6T
- control_v11f1e_sd15_tile↗
lllyasviel
This checkpoint is a conversion of the original checkpoint into `diffusers` format. It can be used in combination with **Stable Diffusion**, such as runwayml/stable-diffusion-v1-5. This checkpoint corresponds to the ControlNet conditioned on **tiled image**. Conceptually, it is similar to a super-resolution model, but its usage is not limited to that. It is also possible to generate details at the same size as the input (conditione) image. @misc{zhang2023adding, title={Adding Conditional Control to Text-to-Image Diffusion Models}, author={Lvmin Zhang and Maneesh Agrawala}, year={2023}, eprint={2302.05543}, archivePrefix={arXiv}, primaryClass={cs.CV} }
- GLM-5.3-GGUF↗
unsloth
You can now run GLM-5.3 in Unsloth Desktop with toggles for Low, High and Max thinking. Read our GLM-5.3 guide for analysis and instructions. GLM-5.3 uses the same base model as GLM-5.2 — every gain comes from post-training. Compared with GLM-5.2, it is much better at complex coding and long-horizon tasks: + Stronger Coding: GLM-5.3 is the most capable open-weights model for coding, with a 50% improvement over GLM-5.2 on our in-house Z.ai Code Bench. It also achieve open-source SOTA on public benchmarks including Terminal Bench 3.0 and Agents' Last Exam. + Emergent Cyber Capability: As we scaled post-training, cyber capability developed faster than we expected. GLM-5.3 is state of the art on CyberGym for vulnerability discovery, and its gains are largest further up the exploitation chain, where it more than doubles GLM-5.2 on exploitation benchmarks.
- LTX-2.5-GGUF↗
unsloth
This is a GGUF quantized version of LTX-2.5 by Lightricks, the 22B audio-video DiT, both the **distilled** (8 + 3 steps, CFG 1) and the **dev** (full, 30 steps, CFG 3) checkpoint. unsloth/LTX-2.5-GGUF uses Unsloth Dynamic 2.0 methodology: the `UD-` files are mixed precision, with every layer's type chosen from a measured sensitivity scan, and they beat the uniform files at the same size. - Loads in Unsloth Studio and in ComfyUI through ComfyUI-GGUF (`Unet Loader (GGUF)`), both tested on every file here. - Same layout as unsloth/LTX-2.3-GGUF: dev files at the root, distilled files in `distilled/`. The text encoder, VAEs and latent upscaler are unchanged; take them from Lightricks/LTX-2.5. **Recommended:** `UD-Q4_K_XL` (14.0 GB) for most GPUs: clearly better than `Q4_K_M` and on par with `Q5_K_M`, which is 1.6 GB larger. For output closest to bf16 use `Q8_0`; on small cards use `UD-Q2_K_XL` (9.5 GB), which is far cleaner than `Q2_K` and close to `Q3_K_M` at 1.8 GB less. `distilled/ltx-2.5-22b-distilled-UD-Q4_K_XL.gguf`, 1280x704, 121 frames, 24 fps, seed 42, with audio: text to video, image to video.